fix: fixed testing and added more examples (#10)

* feat: added new examples and organized existing ones

* fix: fixed testing not correct results

.github/workflows/test.yml

@@ -10,7 +10,24 @@ on:
     branches: [ "*" ]
 
 jobs:
+  check-linting:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python 3.12
+      uses: actions/setup-python@v3
+      with:
+        python-version: "3.12"
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install black
+    - name: Check linting
+      run: black --check .  
+      # If PR fails in this stage, it's because of linting issues: run `black .` to fix them
+
   build:
+    needs: check-linting
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false

examples/broadcasting/README.md

@@ -0,0 +1,46 @@
+# Broadcasting Tutorial
+
+This tutorial shows how to efficiently use broadcasting in Nada using Nada Algebra. 
+
+```python
+from nada_dsl import *
+
+# Step 0: Nada Algebra is imported with this line
+import nada_algebra as na
+
+
+def nada_main():
+    # Step 1: We use Nada Algebra wrapper to create "Party0", "Party1" and "Party2"
+    parties = na.parties(3)
+
+    # Step 2: Party0 creates an array of dimension (3, ) with name "A"
+    a = na.array([3], parties[0], "A")
+
+    # Step 3: Party1 creates an array of dimension (3, ) with name "B"
+    b = na.array([3], parties[1], "B")
+
+    # Step 4: Party0 creates an array of dimension (3, ) with name "C"
+    c = na.array([3], parties[0], "C")
+
+    # Step 5: Party1 creates an array of dimension (3, ) with name "D"
+    d = na.array([3], parties[1], "D")
+
+    # Step 4: The result is of computing SIMD operations on top of the elements of the array
+    # SIMD operations are performed on all the elements of the array.
+    # The equivalent would be: for i in range(3): result += a[i] + b[i] - c[i] * d[i]
+    result = a + b - c * d
+    # Step 5: We can use result.output() to produce the output for Party2 and variable name "my_output"
+    return result.output(parties[2], "my_output")
+```
+
+0. We import Nada algebra using `import nada_algebra as na`.
+1. We create an array of parties, with our wrapper using `parties = na.parties(3)` which creates an array of parties named: `Party0`, `Party1` and `Party2`.
+2. We create our input array `a` with `na.array([3], parties[0], "A")`, meaning our array will have dimension 3, `Party0` will be in charge of giving its inputs and the name of the variable is `"A"`.
+3. We create our input array `b` with `na.array([3], parties[1], "B")`, meaning our array will have dimension 3, `Party1` will be in charge of giving its inputs and the name of the variable is `"B"`.
+4. We create our input array `c` with `na.array([3], parties[1], "C")`, meaning our array will have dimension 3, `Party0` will be in charge of giving its inputs and the name of the variable is `"C"`.
+5. We create our input array `d` with `na.array([3], parties[1], "D")`, meaning our array will have dimension 3, `Party1` will be in charge of giving its inputs and the name of the variable is `"D"`.
+5. Finally, we use Nada Algebra to produce the outputs of the array like:  `result.output(parties[2], "my_output")` establishing that the output party will be `Party2`and the name of the output variable will be `my_output`. 
+# How to run the tutorial.
+
+1. First, we need to compile the nada program running: `nada build`.
+2. Then, we can test our program is running with: `nada test`. 

examples/broadcasting/nada-project.toml

@@ -0,0 +1,7 @@
+name = "broadcasting"
+version = "0.1.0"
+authors = [""]
+
+[[programs]]
+path = "src/broadcasting.py"
+prime_size = 128

examples/broadcasting/network/compute.py

@@ -0,0 +1,118 @@
+# Import necessary libraries and modules
+import asyncio
+import py_nillion_client as nillion
+import os
+import sys
+import pytest
+import numpy as np
+import time
+from dotenv import load_dotenv
+
+# Add the parent directory to the system path to import modules from it
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")))
+
+# Import helper functions for creating nillion client and getting keys
+from dot_product.network.helpers.nillion_client_helper import create_nillion_client
+from dot_product.network.helpers.nillion_keypath_helper import (
+    getUserKeyFromFile,
+    getNodeKeyFromFile,
+)
+import nada_algebra.client as na_client
+
+# Load environment variables from a .env file
+load_dotenv()
+from dot_product.config.parameters import DIM
+
+
+# Main asynchronous function to coordinate the process
+async def main():
+    print(f"USING: {DIM}")
+    cluster_id = os.getenv("NILLION_CLUSTER_ID")
+    userkey = getUserKeyFromFile(os.getenv("NILLION_USERKEY_PATH_PARTY_1"))
+    nodekey = getNodeKeyFromFile(os.getenv("NILLION_NODEKEY_PATH_PARTY_1"))
+    client = create_nillion_client(userkey, nodekey)
+    party_id = client.party_id
+    user_id = client.user_id
+    party_names = na_client.parties(3)
+    program_name = "main"
+    program_mir_path = f"./target/{program_name}.nada.bin"
+
+    # Store the program
+    action_id = await client.store_program(cluster_id, program_name, program_mir_path)
+    program_id = f"{user_id}/{program_name}"
+    print("Stored program. action_id:", action_id)
+    print("Stored program_id:", program_id)
+
+    # Create and store secrets for two parties
+    A = np.ones([DIM])
+    C = np.ones([DIM])
+    stored_secret = nillion.Secrets(
+        na_client.concat(
+            [
+                na_client.array(A, "A"),
+                na_client.array(C, "C"),
+            ]
+        )
+    )
+    secret_bindings = nillion.ProgramBindings(program_id)
+    secret_bindings.add_input_party(party_names[0], party_id)
+
+    # Store the secret for the specified party
+    A_store_id = await client.store_secrets(
+        cluster_id, secret_bindings, stored_secret, None
+    )
+
+    B = np.ones([DIM])
+    D = np.ones([DIM])
+    stored_secret = nillion.Secrets(
+        na_client.concat(
+            [
+                na_client.array(B, "B"),
+                na_client.array(D, "D"),
+            ]
+        )
+    )
+    secret_bindings = nillion.ProgramBindings(program_id)
+    secret_bindings.add_input_party(party_names[1], party_id)
+
+    # Store the secret for the specified party
+    B_store_id = await client.store_secrets(
+        cluster_id, secret_bindings, stored_secret, None
+    )
+
+    # Set up the compute bindings for the parties
+    compute_bindings = nillion.ProgramBindings(program_id)
+    [
+        compute_bindings.add_input_party(party_name, party_id)
+        for party_name in party_names[:-1]
+    ]
+    compute_bindings.add_output_party(party_names[-1], party_id)
+
+    print(f"Computing using program {program_id}")
+    print(f"Use secret store_id: {A_store_id}, {B_store_id}")
+
+    computation_time_secrets = nillion.Secrets({"my_int2": nillion.SecretInteger(10)})
+
+    # Perform the computation and return the result
+    compute_id = await client.compute(
+        cluster_id,
+        compute_bindings,
+        [A_store_id, B_store_id],
+        computation_time_secrets,
+        nillion.PublicVariables({}),
+    )
+
+    # Monitor and print the computation result
+    print(f"The computation was sent to the network. compute_id: {compute_id}")
+    while True:
+        compute_event = await client.next_compute_event()
+        if isinstance(compute_event, nillion.ComputeFinishedEvent):
+            print(f"✅  Compute complete for compute_id {compute_event.uuid}")
+            print(f"🖥️  The result is {compute_event.result.value}")
+            return compute_event.result.value
+    return result
+
+
+# Run the main function if the script is executed directly
+if __name__ == "__main__":
+    asyncio.run(main())

examples/broadcasting/network/helpers/nillion_client_helper.py

@@ -0,0 +1,12 @@
+import os
+import py_nillion_client as nillion
+from helpers.nillion_payments_helper import create_payments_config
+
+
+def create_nillion_client(userkey, nodekey):
+    bootnodes = [os.getenv("NILLION_BOOTNODE_MULTIADDRESS")]
+    payments_config = create_payments_config()
+
+    return nillion.NillionClient(
+        nodekey, bootnodes, nillion.ConnectionMode.relay(), userkey, payments_config
+    )

examples/broadcasting/network/helpers/nillion_keypath_helper.py

@@ -0,0 +1,10 @@
+import os
+import py_nillion_client as nillion
+
+
+def getUserKeyFromFile(userkey_filepath):
+    return nillion.UserKey.from_file(userkey_filepath)
+
+
+def getNodeKeyFromFile(nodekey_filepath):
+    return nillion.NodeKey.from_file(nodekey_filepath)

examples/broadcasting/network/helpers/nillion_payments_helper.py

@@ -0,0 +1,12 @@
+import os
+import py_nillion_client as nillion
+
+
+def create_payments_config():
+    return nillion.PaymentsConfig(
+        os.getenv("NILLION_BLOCKCHAIN_RPC_ENDPOINT"),
+        os.getenv("NILLION_WALLET_PRIVATE_KEY"),
+        int(os.getenv("NILLION_CHAIN_ID")),
+        os.getenv("NILLION_PAYMENTS_SC_ADDRESS"),
+        os.getenv("NILLION_BLINDING_FACTORS_MANAGER_SC_ADDRESS"),
+    )

examples/broadcasting/src/broadcasting.py

@@ -0,0 +1,28 @@
+from nada_dsl import *
+
+# Step 0: Nada Algebra is imported with this line
+import nada_algebra as na
+
+
+def nada_main():
+    # Step 1: We use Nada Algebra wrapper to create "Party0", "Party1" and "Party2"
+    parties = na.parties(3)
+
+    # Step 2: Party0 creates an array of dimension (3, ) with name "A"
+    a = na.array([3], parties[0], "A")
+
+    # Step 3: Party1 creates an array of dimension (3, ) with name "B"
+    b = na.array([3], parties[1], "B")
+
+    # Step 4: Party0 creates an array of dimension (3, ) with name "C"
+    c = na.array([3], parties[0], "C")
+
+    # Step 5: Party1 creates an array of dimension (3, ) with name "D"
+    d = na.array([3], parties[1], "D")
+
+    # Step 4: The result is of computing SIMD operations on top of the elements of the array
+    # SIMD operations are performed on all the elements of the array.
+    # The equivalent would be: for i in range(3): result += a[i] + b[i] - c[i] * d[i]
+    result = a + b - c * d
+    # Step 5: We can use result.output() to produce the output for Party2 and variable name "my_output"
+    return result.output(parties[2], "my_output")

examples/broadcasting/target/.gitignore

@@ -0,0 +1,5 @@
+# This directory is kept purposely, so that no compilation errors arise.
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore

examples/broadcasting/tests/broadcasting.yaml

@@ -0,0 +1,36 @@
+---
+program: broadcasting
+inputs:
+  secrets:
+    B_1:
+      SecretInteger: "3"
+    A_0:
+      SecretInteger: "3"
+    C_1:
+      SecretInteger: "3"
+    B_0:
+      SecretInteger: "3"
+    D_0:
+      SecretInteger: "3"
+    C_2:
+      SecretInteger: "3"
+    C_0:
+      SecretInteger: "3"
+    A_2:
+      SecretInteger: "3"
+    A_1:
+      SecretInteger: "3"
+    D_1:
+      SecretInteger: "3"
+    B_2:
+      SecretInteger: "3"
+    D_2:
+      SecretInteger: "3"
+  public_variables: {}
+expected_outputs:
+  my_output_1:
+    SecretInteger: "-3"
+  my_output_2:
+    SecretInteger: "-3"
+  my_output_0:
+    SecretInteger: "-3"